SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914609035739136 |
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| author | Jose, Edwin |
| author_facet | Jose, Edwin |
| contents | Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Existing approaches either require a trusted central coordinator (cloud marketplaces), demand heavy blockchain infrastructure (Golem, BrokerChain), or lack an incentive layer entirely (BOINC, Petals). We propose SwarmHarness, a decentralised protocol in which HarnessAPI skill nodes self-organise into a compute swarm without any central authority. SwarmHarness has three interlocking components: a SwarmRegistry built on a Distributed Hash Table (DHT) for peer discovery and capability advertisement; a SwarmRouter that dispatches tasks to nodes using a utility function over capability, load, latency, and trust; and SwarmCredit, an incentive mechanism that attributes compute-credit rewards to contributing nodes via a Shapley-value approximation. Nodes earn credits by serving tasks and spend credits to submit them; idle nodes that never contribute drain credits and lose routing priority, creating a self-regulating participation economy. As nodes specialise toward high-reward skills and routing signals act as digital pheromones, the network exhibits emergent collective intelligence analogous to biological swarms. Beyond compute sharing, SwarmHarness is a foundational primitive for autonomous distributed AI agent networks in which agents hire compute, route subtasks, and settle credits without human intermediation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_28764 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks Jose, Edwin Artificial Intelligence Distributed, Parallel, and Cluster Computing Multiagent Systems Vast quantities of compute (GPU cycles on personal workstations, idle inference servers, and edge devices between jobs) go unused because no incentive-aligned protocol exists for their owners to share them safely and profitably. Existing approaches either require a trusted central coordinator (cloud marketplaces), demand heavy blockchain infrastructure (Golem, BrokerChain), or lack an incentive layer entirely (BOINC, Petals). We propose SwarmHarness, a decentralised protocol in which HarnessAPI skill nodes self-organise into a compute swarm without any central authority. SwarmHarness has three interlocking components: a SwarmRegistry built on a Distributed Hash Table (DHT) for peer discovery and capability advertisement; a SwarmRouter that dispatches tasks to nodes using a utility function over capability, load, latency, and trust; and SwarmCredit, an incentive mechanism that attributes compute-credit rewards to contributing nodes via a Shapley-value approximation. Nodes earn credits by serving tasks and spend credits to submit them; idle nodes that never contribute drain credits and lose routing priority, creating a self-regulating participation economy. As nodes specialise toward high-reward skills and routing signals act as digital pheromones, the network exhibits emergent collective intelligence analogous to biological swarms. Beyond compute sharing, SwarmHarness is a foundational primitive for autonomous distributed AI agent networks in which agents hire compute, route subtasks, and settle credits without human intermediation. |
| title | SwarmHarness: Skill-Based Task Routing via Decentralized Incentive-Aligned AI Agent Networks |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing Multiagent Systems |
| url | https://arxiv.org/abs/2605.28764 |